569-04-010 ISMRM Abstract

Deep Learning–Based Reconstruction: Model Comparison for Variable-Density GRAPPA 1H MRSI

Primary: Acquisition & Reconstruction - Spectroscopy and spectroscopic imaging

Secondary: Acquisition & Reconstruction - Image Reconstruction: AI

569-04-010 · Spectroscopy: Acquisition and Reconstruction · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row J

Keywords: AI/ML Image Reconstruction Machine Learning/Artificial Intelligence Anatomically Guided Reconstruction Comparative Study Undersampling

Accepted

Xinyu Zhang ¹, Mahrshi Jani, Andrew Wright, Kimberly L Chan, Anke Henning¹

¹Advanced Imaging Center/UTSW, United States of America

Presenting Author: Xinyu Zhang

Synopsis

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References

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10. Simonyan, Karen & Zisserman, Andrew. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 1409.1556.

11. Zhou, Z., Siddiquee, M. M. R, Tajbakhsh, N. & Liang, J. UNet++: A nested U-Net architecture for medical image segmentation. https://arxiv.org/abs/1807.10165 (2018).

12. Oktay, O. et al. Attention U-Net: Learning where to look for the pancreas. https://arxiv.org/abs/1804.03999 (2018).

13. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition in 2016 IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (IEEE, 2016).

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http://echo.ismrm.org/p/ISMRM2026/569-04-010